| Precision and Recall per Class | ||
| class | precision | recall |
|---|---|---|
| AI | 0.65 | 0.65 |
| Non-AI | 0.59 | 0.59 |
# A tibble: 2 × 3
class precision recall
<fct> <dbl> <dbl>
1 AI 0.686 0.714
2 Non-AI 0.641 0.610
We observe the highest novelty peaks around 10 seconds, 110
seconds, and 160 seconds, which likely indicate structural changes in
the song.
Around 10 seconds, we hear a noticeable stop in the music, aligning with the early peak in the graph. At approximately 115 seconds, the volume increases significantly, reflected by a sharp rise in energy novelty. Around 165 seconds, the volume diminishes, matching the final large peak before a drop in novelty. This suggests, that the signal function is probably accurate and captures the transitions in the music well. It was expected that the signal novelty function would work well, because the song has some dramtic changes.
The spectral novelty function graph is significant less
clear than the energy novelty function graph. Here we see no prominent
cycles, but we can slightly see peeks at 10 seconds and 160 seconds,
which we also saw (but more clear) in the energy novel function. However
an additonal peak can be observed at the end of the track, which is also
hearable in the song, at the end the song slows down and the volume
weakens. We see no clear repeating patterns in the song.
The cyclic tempogram seems more representative we see a
prominent line at 80 BPM, which is suitable for a moderate tempo
emotional piano piece.
In the keygram graph, we can notice the most intense color
values, especially between 0-100 seconds, around C major and G major.
Additionally, E minor appears relatively stable, with less variation in
color intensity, with fewer yellow and greenish hues. This is
interesting because, according to the Circle of Fifths, E minor is the
relative minor of G major. B and E major, and G and C minor are also
noticeable.
In the choreography visualization we can see very intense
values throughout the whole track for E minor, also noticeable is C
major. In the first few second of the track we van see darker hues of B
minor, G major and a bit of B major. Additionally at the ned of the
track we see now light hues, and darker blocks from E major to A:7 and E
minor to A minor.
On this page I am going to compare my two songs to my class corpus. I am unfortunately not a musician, so I decided to generate a song and choose a royalty free song.
Song 1: I generated this song by using Soundful’s free AI music generator. First I had to choose genre, I chose pop rock. Then I had to decide for the speed/BPM. I decided 121 BPM. For chords and scales, I selected F# minor. I wanted to select two different songs, so for this song I chose a somewhat faster and energetic song.
Song 2: I picked this song from the site from Pixabay. Because I had the idea to selected two different songs, I wanted a ‘slower’ song with more calm vibes, opposed to the faster rock song. However towards the end of the song there is a fragment that sound heroic and cinematic, which I find to be an interesting component in this song. Also, I wanted to have a human created song, so I can maybe analyse if there are differences between the AI generated song (I filtered the songs on this site to be strictly human generated). Song: Sweet Dreams by: Lexin_Music.